Large organizations, such as commercial organizations, financial organizations or public safety organizations conduct numerous interactions with customers, users, suppliers or other persons on a daily basis. Many of these interactions are vocal, or at least comprise a vocal component, such as an audio part of a video or face-to-face interaction. In order to get insight into the data conveyed by these interactions, the interactions are captured and often recorded. In some cases, quality monitoring is performed for assessing the quality of the agent handling the interaction or another entity associated with the call center like a product, the organization, or the like. Quality monitoring is performed either manually by listening to interactions, or by automated systems.
Automated systems activate multiple tools as part of the analysis. Such tools may include voice recognition tools such as automatic speech recognition or word spotting, emotion analysis tools, call flow analysis, including for example interaction duration, hold time, number of transfers or the like.
However, even if full and accurate transcription and additional data related to an interaction is available, this still leaves many questions unanswered, such as what makes interactions handled by a particular agent more effective than those handled by others, why is the average duration of interactions handled by one agent longer than average, or others.
There is thus a need in the art for a method and system for deep interaction analysis, in order to uncover further insights from the interactions, beyond the data available from voice analysis tools. Such analysis will provide better understanding and enable improvements in interaction handling in call centers or other interaction-rich environments.